The Big Data of Union Pacific’s rolling assets

For example, UP would like to be able to predict when a wheel is going to fail weeks before it causes a 1.5-mile-long, 20,000-ton coal train running at 70 miles per hour to derail, risking lives, causing delays, and losing the company money. UP has been using technology to predict and prevent derailments for well over a decade. It has placed infrared sensors every 20 miles on its tracks to take about 20 million temperature readings of train wheels a day to look for overheating, a sign of impending failure. It has trackside microphones to listen for growling bearings in the wheels. For its biggest and heaviest coal trains, it even shoots ultrasound images--like those used to see a fetus in the womb--to look for flaws inside wheels.

Data is sent via fiber-optic cables that run alongside UP's tracks back to its Omaha-area data centers. There, complex pattern-matching algorithms flag the outliers, letting experts decide within five minutes of taking a reading whether a driver should pull a train off the track for inspection, or perhaps just slow it from 70 to 35 mph until it can be repaired at the next station. Using all of these technologies, UP has cut bearing-related derailments by 75%.